package ai.knowly.langtorch.llm.openai.schema.dto.completion.chat;

import ai.knowly.langtorch.schema.chat.ChatMessage;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.List;
import java.util.Map;
import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.Data;
import lombok.NoArgsConstructor;

@Data
@Builder(toBuilder = true, setterPrefix = "set")
@AllArgsConstructor
@NoArgsConstructor
public class ChatCompletionRequest {

  /** ID of the model to use. */
  String model;

  /**
   * The messages to generate chat completions for, in the <a
   * href="https://platform.openai.com/docs/guides/chat/introduction">chat format</a>.<br>
   * see {@link com.theokanning.openai.completion.chat.ChatMessage}
   */
  List<ChatMessage> messages;

  /**
   * What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output
   * more random, while lower values like 0.2 will make it more focused and deterministic.<br>
   * We generally recommend altering this or top_p but not both.
   */
  Double temperature;

  /**
   * An alternative to sampling with temperature, called nucleus sampling, where the model considers
   * the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising
   * the top 10% probability mass are considered.<br>
   * We generally recommend altering this or temperature but not both.
   */
  @JsonProperty("top_p")
  Double topP;

  /** How many chat completion chatCompletionChoices to generate for each input message. */
  Integer n;

  /**
   * If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only
   * <a
   * href="https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format">server-sent
   * events</a> as they become available, with the stream terminated by a data: [DONE] message.
   */
  Boolean stream;

  /** Up to 4 sequences where the API will stop generating further tokens. */
  List<String> stop;

  /**
   * The maximum number of tokens allowed for the generated answer. By default, the number of tokens
   * the model can return will be (4096 - prompt tokens).
   */
  @JsonProperty("max_tokens")
  Integer maxTokens;

  /**
   * Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear
   * in the text so far, increasing the model's likelihood to talk about new topics.
   */
  @JsonProperty("presence_penalty")
  Double presencePenalty;

  /**
   * Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing
   * frequency in the text so far, decreasing the model's likelihood to repeat the same line
   * verbatim.
   */
  @JsonProperty("frequency_penalty")
  Double frequencyPenalty;

  /**
   * Accepts a json object that maps tokens (specified by their token ID in the tokenizer) to an
   * associated bias value from -100 to 100. Mathematically, the bias is added to the logits
   * generated by the model prior to sampling. The exact effect will vary per model, but values
   * between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100
   * should result in a ban or exclusive selection of the relevant token.
   */
  @JsonProperty("logit_bias")
  Map<String, Integer> logitBias;

  /**
   * A unique identifier representing your end-user, which will help OpenAI to monitor and detect
   * abuse.
   */
  String user;

  private List<Function> functions;

  @JsonProperty("function_call")
  private Object functionCall;
}
